Prediction of local elasto-plastic stress and strain fields in a two-phase composite microstructure using a deep convolutional neural network

Design and analysis of inelastic materials requires prediction of physical responses that evolve under loading. Numerical simulation of such behavior using finite element (FE) approaches can call for significant time and computational effort. To address this challenge, this paper demonstrates a deep learning (DL) framework that is capable of predicting micro-scale elasto-plastic strains and stresses in a two-phase medium, at a much greater speed than traditional FE simulations. The proposed framework uses a deep convolutional neural network (CNN), specifically a U-Net architecture with 3D operations, to map the composite microstructure to the corresponding stress and strain fields under a predetermined load path. In particular, the model is applied to a two-phase fiber reinforced plastic (FRP) composite microstructure subjected to a given loading-unloading path, predicting the corresponding stress and strain fields at discrete intermediate load steps. A novel two-step training approach provides more accurate predictions of stress, by first training the model to predict strain fields and then using those strain fields as input to the model that predicts the stress fields. This efficient data-driven approach enables accurate prediction of physical fields in inelastic materials, based solely on microstructure images and loading information.Comment: 24 pages, 21 figure

The presence of a dog increases greetings: a study in social intervention

Pet dogs served many useful purposes, especially by provoking social interaction in the lives of their owners. The purpose of this study was to show that a person accompanied by a dog received more greetings from passersby than a person without a dog. This was a field study using a female confederate, a dog, and three observers. Subjects passing in front of the confederate were recorded as a greeter or a non-greeter. Results formulated by the Chi Square showed that when the confederate was with the dog, they were greeted significantly more than when they were not accompanied by the dog. This study had important applications for people who consider themselves lonely, shy, or socially inept

From research to practice: the effect of multi-component vocabulary instruction on fourth grade students' social studies vocabulary and comprehension performance

This study was designed to demonstrate the effect of implementation of multicomponent vocabulary strategy instruction in fourth grade social studies. The components used included explicit instruction, student study teams, active engagement in learning tasks, vocabulary maps, connections webs, and semantic feature analysis. The focus was on using direct, explicit instruction of vocabulary strategies and the resulting outcomes. Curriculum was designed for a six-week period using the district curriculum and state-required knowledge and skills for fourth graders. Teachers were randomly chosen for assignment to the group receiving the intervention and/or to the control group. The curriculum for this study was designed to actively engage students and to reinforce retention of word meanings in isolation as well as in context. The study included three different school districts, five separate campuses, and a total of 375 students in grade four. There were 23 teachers in the study with students in 29 separate classes. Measures were employed to determine if there was an effect on the students in the classrooms receiving the intervention versus those receiving regular classroom instruction. Measures used included a comprehension test, a content test, a curriculum-based measure, checkpoints for content, similar to a unit test, the TORC3 vocabulary subtest for social studies, and the Test of Silent Contextual Reading Fluency (TOSCRF). A preliminary analysis included reliability coefficients of all instruments used in the study. Difference score analyses and descriptive statistics, along with a one-way multivariate analysis of variance (MANOVA) and a repeated measures MANOVA were completed using the effect for group, effect for time, and the interaction effect. The final analysis included a plot of classroom means for each of the instruments used in the study. Outcomes were consistent across all administered measures. Although growth was demonstrated in both the group receiving the intervention and the group receiving regular classroom instruction, the gains were consistently greater overall with the classrooms receiving the intervention. Experimenting with practices to determine their effectiveness is critical for improving classroom instruction, and this study demonstrated that students were retaining knowledge even after six weeks post-intervention

Model predicts catastrophic decline of common bottlenose dolphin (Tursiops truncatus) population under proposed land restoration project in Barataria Bay, Louisiana, USA

Funding: Fundação para a Ciência e a Tecnologia (Grant Number(s): UIDB/00006/2020).Publisher PDFPeer reviewe

Virus-Induced Gene Silencing and Transient Gene Expression in Soybean (Glycine max) Using Bean Pod Mottle Virus Infectious Clones

Virus-induced gene silencing (VIGS) is a powerful and rapid approach for determining the functions of plant genes. The basis of VIGS is that a viral genome is engineered so that it can carry fragments of plant genes, typically in the 200 to 300 base pair size range. The recombinant viruses are used to infect experimental plants, and wherever the virus invades, the target gene or genes will be silenced. VIGS is thus transient, and in the span of a few weeks, it is possible to design VIGS constructs and then generate loss-of-function phenotypes through RNA silencing of the target genes. In soybean (Glycine max), the Bean pod mottle virus (BPMV) has been engineered to be valuable tool for silencing genes with diverse functions and also for over-expression of foreign genes. This protocol describes a method for designing BPMV constructs and using them to silence or transiently express genes in soybean

Surrogate modeling and model selection in irreducible high dimensions with small sample size

There exist a number of high dimensional problems in which the dimensions cannot be effectively reduced, since all of them are more or less equally important. On top of that, when the computational models are expensive, it is not practical to perform more than a small number of model evaluations. In situations like this, a good space filling design is needed that provides maximum coverage over the input domain. In surrogate modeling methods, like kriging interpolation or radial basis function interpolation, a good sampling design can help improve the condition number of the kernel matrix by placing samples as far apart from each other as possible. In this study, the performance of three hierarchical space filled designs, namely Refined Latinized Stratified Sampling (RLSS), Hierarchical Latin Hypercube Sampling (HLHS) and Sobol quasi-random sequence, are compared using the Rosenbrock function in different dimensions. Ordinary kriging interpolation is chosen as the surrogate modeling method with different choices of correlation functions. The AIC criterion is used for model selection and the accuracy of selection is cross-verified using the root mean squared (RMS) error values